Residual enzymatic activity as a prognostic factor in patients with Gaucher disease type 1: Correlation with Zimran and GAUSS-I index and the severity of bone disease

Background: Gaucher disease (GD) is an autosomal recessive disorder produced by mutations in the glucocerebrosidase gene (GBA), causing storage of glucosylceramide in reticuloendothelial cells in multiple organs. Traditionally, the prediction of the phenotype based on the genotype has been reported to be limited.Subjects and Methods: We investigated the correlation between the enzymatic residual activity (ERA) and the phenotype at diagnosis of the disease in 45 GD Spanish patients (44 with type I and 1 with type III GD). The genotype involved two of the following previously expressed proteins: c.517A¿>¿C (T134P), 1%; c.721G¿>¿A (G202R), 17%; c.1090G¿>¿T (G325W), 13.9%; c.1208G¿>¿A (S364N), 4.1%; c.1226A¿>¿G (N370S), 17.8%; c.1246G¿>¿A (G377S), 17.6%; c.1289C¿>¿T (P391L), 8.5%; c.1448T¿>¿C (L444P), 3%; and c.1504C¿>¿T (R463C), 24.5%. Recombinant alleles, deletion of 55¿bp in exon 9 and 84GG mutation were considered as mutations with no residual enzymatic activity.Results: The ERA showed a statistically significant correlation with chitotriosidase (P¿<¿0.001), age (P¿<¿0.001), spleen size (P¿<¿0.001), ‘Zimran’s Severity Score Index’ (P¿<¿0.01) and the ‘Gaucher Disease Severity Score Index—Type I’ (P¿<¿ 0.0001) at diagnosis of the disorder. Previous to any medical intervention, a comparison between the ERA and bone involvement, demonstrated a statistically significant relationship (P¿<¿0.01) between the two variables.Conclusions: This study data allowed us to define a new criterion for prognostic assessment of the disease at diagnosis, called Protein Severity Index, which expresses the theoretical severity of the genotype presented by patients, according to the corresponding ERA

Adaptive Radiation in Mediterranean Cistus (Cistaceae)

lineage consists of 12 species primarily distributed in Mediterranean habitats and is herein subject to analysis. lineages), which display asymmetric characteristics: number of species (2 vs. 10), leaf morphologies (linear vs. linear to ovate), floral characteristics (small, three-sepalled vs. small to large, three- or five-sepalled flowers) and ecological attributes (low-land vs. low-land to mountain environments). A positive phenotype-environment correlation has been detected by historical reconstructions of morphological traits (leaf shape, leaf labdanum content and leaf pubescence). Ecological evidence indicates that modifications of leaf shape and size, coupled with differences in labdanum secretion and pubescence density, appear to be related to success of new species in different Mediterranean habitats.

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

Geographic patterns in fruit colour diversity: do leaves constrain the colour of fleshy fruits?

7 pages, 2 figures.-- Supplementary material available: The list of fruit species, their colour as perceived by humans, their provenance and main disperser types. The dispersal category "mixed" refers to species consumed by birds and mammals (XLS, 43 kb).We tested for geographic patterns in fruit colour diversity. Fruit colours are thought to promote detection by seed dispersers. Because seed dispersers differ in their spectral sensitivities, we predicted that fruit colour diversity would be higher in regions with higher seed disperser diversity (i.e. the tropics). We collected reflectance data on 232 fruiting plant species and their natural backgrounds in seven localities in Europe, North and South America, and analysed fruit colour diversity according to the visual system of birds—the primary consumer types of these fruits. We found no evidence that fruit colours are either more conspicuous or more diverse in tropical areas characterised by higher seed disperser diversity. Instead, fruit colour diversity was lowest in central Brazil, suggesting that fruit colours may be more diverse in temperate regions. Although we found little evidence for geographic variation in fruit hues, the spectral properties of fruits were positively associated with the spectral properties of backgrounds. This result implies that fruit colours may be influenced by selection on the reflectance properties of leaves, thus constraining the evolution of fruit colour. Overall, the results suggest that fruit colours in the tropics are neither more diverse nor more conspicuous than temperate fruits, and that fruit colours may be influenced by correlated selection on leaf reflectance properties.H.M.S. was sponsored by a Deutsche Forschungsgemeinschaft (DFG) grant (Scha 1008/4-1). E.C. was sponsored by Fundaçao de Amparo à Pesquisa do Estado de Sao Paulo (Fapesp) and a Deutscher Akademischer Austausch Dienst (DAAD) fellowship. M.G. was sponsored by Fapesp and receives a research fellowship from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and E.C. a Fapesp fellowship. A.V. was supported by the Marie Curie European programme (grant MERG-CT-2004-510260), I3P [Consejo Superior de Investigaciones Científicas (CSIC)] and Acción Integrada (HA2006-0038; Ministerio de Educación y Ciencia).Peer reviewe

Use of eltrombopag for secondary immune thrombocytopenia in clinical practice.

Eltrombopag is a second-line treatment in primary immune thrombocytopenia (ITP). However, its role in secondary ITP is unknown. We evaluated the efficacy and safety of eltrombopag in secondary ITP in daily clinical practice. Eighty-seven secondary ITP patients (46 with ITP secondary to autoimmune syndromes, 23 with ITP secondary to a neoplastic disease subtype: lymphoproliferative disorders [LPDs] and 18 with ITP secondary to viral infections) who had been treated with eltrombopag were retrospectively evaluated. Forty-four patients (38%) had a platelet response, including 40 (35%) with complete responses. Median time to platelet response was 15 days (95% confidence interval, 7-28 days), and was longer in the LPD-ITP group. Platelet response rate was significantly lower in the LPD-ITP than in other groups. However, having achieved response, there were no significant differences between the durable response of the groups. Forty-three patients (49·4%) experienced adverse events (mainly grade 1-2), the commonest being hepatobiliary laboratory abnormalities. There were 10 deaths in this case series, all of which were related to pre-existing medical conditions. In routine clinical practice, eltrombopag is effective and well-tolerated in unselected patients with ITP secondary to both immune and infectious disorders. However, the response rate in LPD-ITP is low

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)